Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations97057
Missing cells99657
Missing cells (%)4.1%
Duplicate rows37
Duplicate rows (%)< 0.1%
Total size in memory87.5 MiB
Average record size in memory945.4 B

Variable types

Numeric10
DateTime1
Text8
Categorical2
Unsupported4

Alerts

Dataset has 37 (< 0.1%) duplicate rowsDuplicates
4Digit is highly overall correlated with HSCodeHigh correlation
FOB INR is highly overall correlated with Item_No and 2 other fieldsHigh correlation
HSCode is highly overall correlated with 4DigitHigh correlation
Item_No is highly overall correlated with FOB INR and 1 other fieldsHigh correlation
Item_Rate_INR is highly overall correlated with Item_Rate_INVHigh correlation
Item_Rate_INV is highly overall correlated with Item_Rate_INRHigh correlation
Quantity is highly overall correlated with FOB INR and 1 other fieldsHigh correlation
Total_Amount_INV_FC is highly overall correlated with FOB INR and 2 other fieldsHigh correlation
Currency is highly imbalanced (73.5%) Imbalance
Address2 has 97057 (100.0%) missing values Missing
IEC_PIN has 2341 (2.4%) missing values Missing
Quantity is highly skewed (γ1 = 47.67407942) Skewed
Item_Rate_INV is highly skewed (γ1 = 84.21398735) Skewed
Total_Amount_INV_FC is highly skewed (γ1 = 119.3453919) Skewed
FOB INR is highly skewed (γ1 = 32.40570545) Skewed
Item_Rate_INR is highly skewed (γ1 = 99.11496921) Skewed
Address2 is an unsupported type, check if it needs cleaning or further analysis Unsupported
ForeignCompany is an unsupported type, check if it needs cleaning or further analysis Unsupported
Invoice_No is an unsupported type, check if it needs cleaning or further analysis Unsupported
IEC_PIN is an unsupported type, check if it needs cleaning or further analysis Unsupported
FOB INR has 9837 (10.1%) zeros Zeros
Item_Rate_INR has 11676 (12.0%) zeros Zeros

Reproduction

Analysis started2025-03-28 10:00:04.810456
Analysis finished2025-03-28 10:00:20.567959
Duration15.76 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

BillNO
Real number (ℝ)

Distinct39998
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8241919.8
Minimum5876961
Maximum8603453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2025-03-28T11:00:20.655958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum5876961
5-th percentile8012610
Q18143522
median8297840
Q38461046
95-th percentile8578895
Maximum8603453
Range2726492
Interquartile range (IQR)317524

Descriptive statistics

Standard deviation419787.36
Coefficient of variation (CV)0.050933201
Kurtosis18.207012
Mean8241919.8
Median Absolute Deviation (MAD)156968
Skewness-4.0240342
Sum7.9993601 × 1011
Variance1.7622143 × 1011
MonotonicityNot monotonic
2025-03-28T11:00:20.807458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8198408 220
 
0.2%
6006245 219
 
0.2%
6002828 213
 
0.2%
6064288 213
 
0.2%
8255517 212
 
0.2%
8285915 189
 
0.2%
8148377 161
 
0.2%
8028523 156
 
0.2%
8142695 154
 
0.2%
8275468 146
 
0.2%
Other values (39988) 95174
98.1%
ValueCountFrequency (%)
5876961 1
 
< 0.1%
5882473 1
 
< 0.1%
5885189 2
 
< 0.1%
5885228 1
 
< 0.1%
5889366 4
 
< 0.1%
5890450 2
 
< 0.1%
5892079 12
< 0.1%
5892155 1
 
< 0.1%
5895260 1
 
< 0.1%
5896224 1
 
< 0.1%
ValueCountFrequency (%)
8603453 1
 
< 0.1%
8603452 5
< 0.1%
8603451 1
 
< 0.1%
8603450 1
 
< 0.1%
8603449 1
 
< 0.1%
8603447 1
 
< 0.1%
8603446 1
 
< 0.1%
8603443 1
 
< 0.1%
8603437 1
 
< 0.1%
8603434 1
 
< 0.1%

4Digit
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2980.1134
Minimum2901
Maximum3006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2025-03-28T11:00:20.946459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2901
5-th percentile2914
Q12941
median3004
Q33004
95-th percentile3004
Maximum3006
Range105
Interquartile range (IQR)63

Descriptive statistics

Standard deviation36.27966
Coefficient of variation (CV)0.012173919
Kurtosis-0.95351131
Mean2980.1134
Median Absolute Deviation (MAD)0
Skewness-0.93888877
Sum2.8924087 × 108
Variance1316.2137
MonotonicityNot monotonic
2025-03-28T11:00:21.088457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3004 57904
59.7%
2933 5439
 
5.6%
2942 4505
 
4.6%
3003 4477
 
4.6%
3005 1697
 
1.7%
2915 1649
 
1.7%
3006 1537
 
1.6%
2941 1530
 
1.6%
2922 1398
 
1.4%
3002 1363
 
1.4%
Other values (39) 15558
 
16.0%
ValueCountFrequency (%)
2901 32
 
< 0.1%
2902 325
 
0.3%
2903 484
0.5%
2904 349
0.4%
2905 833
0.9%
2906 508
0.5%
2907 450
0.5%
2908 99
 
0.1%
2909 700
0.7%
2910 57
 
0.1%
ValueCountFrequency (%)
3006 1537
 
1.6%
3005 1697
 
1.7%
3004 57904
59.7%
3003 4477
 
4.6%
3002 1363
 
1.4%
3001 77
 
0.1%
3000 2
 
< 0.1%
2942 4505
 
4.6%
2941 1530
 
1.6%
2940 127
 
0.1%

Date
Date

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size758.4 KiB
Minimum2016-06-01 00:00:00
Maximum2016-06-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-28T11:00:21.219457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:21.349459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

HSCode
Real number (ℝ)

High correlation 

Distinct762
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29807567
Minimum29011000
Maximum30069200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2025-03-28T11:00:21.484959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum29011000
5-th percentile29147090
Q129411090
median30049011
Q330049099
95-th percentile30049099
Maximum30069200
Range1058200
Interquartile range (IQR)638009

Descriptive statistics

Standard deviation364328.76
Coefficient of variation (CV)0.012222694
Kurtosis-0.95710324
Mean29807567
Median Absolute Deviation (MAD)4921
Skewness-0.937508
Sum2.893033 × 1012
Variance1.3273545 × 1011
MonotonicityNot monotonic
2025-03-28T11:00:21.633458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30049099 28274
29.1%
30049011 4197
 
4.3%
29420090 3958
 
4.1%
30033900 2326
 
2.4%
30049079 1847
 
1.9%
30041090 1764
 
1.8%
30042099 1726
 
1.8%
29339900 1554
 
1.6%
30049069 1442
 
1.5%
30045090 1405
 
1.4%
Other values (752) 48564
50.0%
ValueCountFrequency (%)
29011000 13
 
< 0.1%
29012400 4
 
< 0.1%
29012910 1
 
< 0.1%
29012990 14
 
< 0.1%
29021100 5
 
< 0.1%
29021900 41
< 0.1%
29022000 5
 
< 0.1%
29023000 14
 
< 0.1%
29024100 6
 
< 0.1%
29024300 1
 
< 0.1%
ValueCountFrequency (%)
30069200 9
 
< 0.1%
30069100 4
 
< 0.1%
30067000 94
 
0.1%
30066030 8
 
< 0.1%
30066020 88
 
0.1%
30066010 192
 
0.2%
30065000 61
 
0.1%
30064000 503
0.5%
30063000 74
 
0.1%
30062000 10
 
< 0.1%
Distinct61992
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Memory size10.1 MiB
2025-03-28T11:00:22.087958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length120
Median length88
Mean length59.624118
Min length3

Characters and Unicode

Total characters5786938
Distinct characters91
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45907 ?
Unique (%)47.3%

Sample

1st rowETHYLENE GLYCOL DISTERATE (Assay 98% minimum) (EGDS)
2nd rowCOSMOSIL IM
3rd rowMEBEVERINE HCL BP
4th rowWE INTEND TO CLAIM REWARDS UNDER MERCHANDISE EXPORTS FROM INDIA SCHEME (MEIS)
5th row"Payflex-M-80" Di Octyl Maleate Ester, C
ValueCountFrequency (%)
28363
 
3.7%
tablets 12895
 
1.7%
to 9398
 
1.2%
mg 8157
 
1.1%
of 7483
 
1.0%
usp 7248
 
0.9%
meis 6240
 
0.8%
no 6155
 
0.8%
under 6034
 
0.8%
claim 5641
 
0.7%
Other values (72983) 676256
87.4%
2025-03-28T11:00:22.702459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
700082
 
12.1%
E 401954
 
6.9%
A 365063
 
6.3%
I 306647
 
5.3%
T 278286
 
4.8%
O 255433
 
4.4%
N 238778
 
4.1%
R 236235
 
4.1%
S 228937
 
4.0%
L 220164
 
3.8%
Other values (81) 2555359
44.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3975724
68.7%
Space Separator 700082
 
12.1%
Decimal Number 537085
 
9.3%
Lowercase Letter 207789
 
3.6%
Other Punctuation 160006
 
2.8%
Open Punctuation 76984
 
1.3%
Close Punctuation 68650
 
1.2%
Dash Punctuation 53840
 
0.9%
Math Symbol 6243
 
0.1%
Modifier Symbol 204
 
< 0.1%
Other values (2) 331
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 401954
 
10.1%
A 365063
 
9.2%
I 306647
 
7.7%
T 278286
 
7.0%
O 255433
 
6.4%
N 238778
 
6.0%
R 236235
 
5.9%
S 228937
 
5.8%
L 220164
 
5.5%
M 217862
 
5.5%
Other values (16) 1226365
30.8%
Lowercase Letter
ValueCountFrequency (%)
e 22611
 
10.9%
a 18414
 
8.9%
i 17670
 
8.5%
n 15214
 
7.3%
o 15050
 
7.2%
t 14304
 
6.9%
r 13867
 
6.7%
l 11656
 
5.6%
s 9632
 
4.6%
d 9104
 
4.4%
Other values (16) 60267
29.0%
Other Punctuation
ValueCountFrequency (%)
. 59507
37.2%
: 25922
16.2%
/ 25056
15.7%
, 23854
14.9%
' 8658
 
5.4%
% 5893
 
3.7%
& 5588
 
3.5%
" 2889
 
1.8%
# 1114
 
0.7%
; 716
 
0.4%
Other values (3) 809
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 172049
32.0%
1 103857
19.3%
2 60867
 
11.3%
5 54322
 
10.1%
3 34341
 
6.4%
6 31282
 
5.8%
4 26592
 
5.0%
9 19955
 
3.7%
8 17158
 
3.2%
7 16662
 
3.1%
Math Symbol
ValueCountFrequency (%)
+ 3326
53.3%
= 2888
46.3%
> 27
 
0.4%
< 2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 71804
93.3%
[ 5060
 
6.6%
{ 120
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 63945
93.1%
] 4604
 
6.7%
} 101
 
0.1%
Modifier Symbol
ValueCountFrequency (%)
` 197
96.6%
^ 7
 
3.4%
Space Separator
ValueCountFrequency (%)
700082
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53840
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 167
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4183513
72.3%
Common 1603425
 
27.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 401954
 
9.6%
A 365063
 
8.7%
I 306647
 
7.3%
T 278286
 
6.7%
O 255433
 
6.1%
N 238778
 
5.7%
R 236235
 
5.6%
S 228937
 
5.5%
L 220164
 
5.3%
M 217862
 
5.2%
Other values (42) 1434154
34.3%
Common
ValueCountFrequency (%)
700082
43.7%
0 172049
 
10.7%
1 103857
 
6.5%
( 71804
 
4.5%
) 63945
 
4.0%
2 60867
 
3.8%
. 59507
 
3.7%
5 54322
 
3.4%
- 53840
 
3.4%
3 34341
 
2.1%
Other values (29) 228811
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5786938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
700082
 
12.1%
E 401954
 
6.9%
A 365063
 
6.3%
I 306647
 
5.3%
T 278286
 
4.8%
O 255433
 
4.4%
N 238778
 
4.1%
R 236235
 
4.1%
S 228937
 
4.0%
L 220164
 
3.8%
Other values (81) 2555359
44.2%

Quantity
Real number (ℝ)

High correlation  Skewed 

Distinct15078
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41652.891
Minimum0.001
Maximum53019608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2025-03-28T11:00:22.833958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile1
Q115
median264
Q34315
95-th percentile59670.8
Maximum53019608
Range53019608
Interquartile range (IQR)4300

Descriptive statistics

Standard deviation532786.57
Coefficient of variation (CV)12.791107
Kurtosis3422.5653
Mean41652.891
Median Absolute Deviation (MAD)263
Skewness47.674079
Sum4.0427046 × 109
Variance2.8386153 × 1011
MonotonicityNot monotonic
2025-03-28T11:00:22.972461image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7722
 
8.0%
100 2902
 
3.0%
50 2323
 
2.4%
10 2312
 
2.4%
200 1851
 
1.9%
20 1824
 
1.9%
1000 1767
 
1.8%
5 1703
 
1.8%
2 1632
 
1.7%
500 1579
 
1.6%
Other values (15068) 71442
73.6%
ValueCountFrequency (%)
0.001 1406
1.4%
0.002 27
 
< 0.1%
0.003 12
 
< 0.1%
0.004 14
 
< 0.1%
0.005 73
 
0.1%
0.006 7
 
< 0.1%
0.007 11
 
< 0.1%
0.008 9
 
< 0.1%
0.009 4
 
< 0.1%
0.01 965
1.0%
ValueCountFrequency (%)
53019608 1
< 0.1%
50980392 1
< 0.1%
43779000 1
< 0.1%
42000000 1
< 0.1%
35280000 1
< 0.1%
29505000 1
< 0.1%
25000000 1
< 0.1%
24750000 1
< 0.1%
22522000 1
< 0.1%
21358080 1
< 0.1%

Unit
Categorical

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
NOS
26950 
PAC
23327 
KGS
23222 
PCS
6011 
MTS
2976 
Other values (32)
14571 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters291171
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowKGS
2nd rowKGS
3rd rowKGS
4th rowNOS
5th rowKGS

Common Values

ValueCountFrequency (%)
NOS 26950
27.8%
PAC 23327
24.0%
KGS 23222
23.9%
PCS 6011
 
6.2%
MTS 2976
 
3.1%
BOX 2742
 
2.8%
VLS 2401
 
2.5%
GMS 2391
 
2.5%
LOT 2239
 
2.3%
BTL 1710
 
1.8%
Other values (27) 3088
 
3.2%

Length

2025-03-28T11:00:23.098457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nos 26950
27.8%
pac 23327
24.0%
kgs 23222
23.9%
pcs 6011
 
6.2%
mts 2976
 
3.1%
box 2742
 
2.8%
vls 2401
 
2.5%
gms 2391
 
2.5%
lot 2239
 
2.3%
btl 1710
 
1.8%
Other values (27) 3088
 
3.2%

Most occurring characters

ValueCountFrequency (%)
S 64661
22.2%
O 32258
11.1%
C 30679
10.5%
P 29352
10.1%
N 28283
9.7%
G 25675
 
8.8%
A 23376
 
8.0%
K 23243
 
8.0%
T 9553
 
3.3%
L 6718
 
2.3%
Other values (11) 17373
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 291171
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 64661
22.2%
O 32258
11.1%
C 30679
10.5%
P 29352
10.1%
N 28283
9.7%
G 25675
 
8.8%
A 23376
 
8.0%
K 23243
 
8.0%
T 9553
 
3.3%
L 6718
 
2.3%
Other values (11) 17373
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 291171
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 64661
22.2%
O 32258
11.1%
C 30679
10.5%
P 29352
10.1%
N 28283
9.7%
G 25675
 
8.8%
A 23376
 
8.0%
K 23243
 
8.0%
T 9553
 
3.3%
L 6718
 
2.3%
Other values (11) 17373
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 291171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 64661
22.2%
O 32258
11.1%
C 30679
10.5%
P 29352
10.1%
N 28283
9.7%
G 25675
 
8.8%
A 23376
 
8.0%
K 23243
 
8.0%
T 9553
 
3.3%
L 6718
 
2.3%
Other values (11) 17373
 
6.0%

Item_Rate_INV
Real number (ℝ)

High correlation  Skewed 

Distinct13015
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2117.2733
Minimum0
Maximum10346959
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2025-03-28T11:00:23.221458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q10.8
median4.45
Q343.9
95-th percentile1347.7217
Maximum10346959
Range10346959
Interquartile range (IQR)43.1

Descriptive statistics

Standard deviation74306.097
Coefficient of variation (CV)35.095184
Kurtosis8750.7367
Mean2117.2733
Median Absolute Deviation (MAD)4.4
Skewness84.213987
Sum2.054962 × 108
Variance5.521396 × 109
MonotonicityNot monotonic
2025-03-28T11:00:23.365459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001 2826
 
2.9%
0.0001 1686
 
1.7%
1 × 10-51564
 
1.6%
1 1250
 
1.3%
2 870
 
0.9%
0.01 833
 
0.9%
0.5 657
 
0.7%
10 566
 
0.6%
5 512
 
0.5%
1.5 504
 
0.5%
Other values (13005) 85789
88.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 × 10-51564
1.6%
2 × 10-513
 
< 0.1%
3 × 10-513
 
< 0.1%
4 × 10-56
 
< 0.1%
5 × 10-58
 
< 0.1%
6 × 10-51
 
< 0.1%
8 × 10-51
 
< 0.1%
9 × 10-57
 
< 0.1%
0.0001 1686
1.7%
ValueCountFrequency (%)
10346959 1
 
< 0.1%
9044224 1
 
< 0.1%
6685303.67 2
 
< 0.1%
5277000 1
 
< 0.1%
5219000 2
 
< 0.1%
3493063 8
< 0.1%
2992500 1
 
< 0.1%
2818550 1
 
< 0.1%
2726000 2
 
< 0.1%
2291941 1
 
< 0.1%

Currency
Categorical

Imbalance 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
USD
77035 
INR
8514 
EUR
8256 
GBP
 
1472
AUD
 
605
Other values (14)
 
1175

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters291171
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 77035
79.4%
INR 8514
 
8.8%
EUR 8256
 
8.5%
GBP 1472
 
1.5%
AUD 605
 
0.6%
CAD 270
 
0.3%
SGD 217
 
0.2%
ZAR 205
 
0.2%
RUR 203
 
0.2%
NZD 84
 
0.1%
Other values (9) 196
 
0.2%

Length

2025-03-28T11:00:23.489458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd 77035
79.4%
inr 8514
 
8.8%
eur 8256
 
8.5%
gbp 1472
 
1.5%
aud 605
 
0.6%
cad 270
 
0.3%
sgd 217
 
0.2%
zar 205
 
0.2%
rur 203
 
0.2%
nzd 84
 
0.1%
Other values (9) 196
 
0.2%

Most occurring characters

ValueCountFrequency (%)
U 86154
29.6%
D 78261
26.9%
S 77252
26.5%
R 17436
 
6.0%
N 8648
 
3.0%
I 8514
 
2.9%
E 8304
 
2.9%
G 1689
 
0.6%
B 1527
 
0.5%
P 1506
 
0.5%
Other values (11) 1880
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 291171
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 86154
29.6%
D 78261
26.9%
S 77252
26.5%
R 17436
 
6.0%
N 8648
 
3.0%
I 8514
 
2.9%
E 8304
 
2.9%
G 1689
 
0.6%
B 1527
 
0.5%
P 1506
 
0.5%
Other values (11) 1880
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 291171
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 86154
29.6%
D 78261
26.9%
S 77252
26.5%
R 17436
 
6.0%
N 8648
 
3.0%
I 8514
 
2.9%
E 8304
 
2.9%
G 1689
 
0.6%
B 1527
 
0.5%
P 1506
 
0.5%
Other values (11) 1880
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 291171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 86154
29.6%
D 78261
26.9%
S 77252
26.5%
R 17436
 
6.0%
N 8648
 
3.0%
I 8514
 
2.9%
E 8304
 
2.9%
G 1689
 
0.6%
B 1527
 
0.5%
P 1506
 
0.5%
Other values (11) 1880
 
0.6%

Total_Amount_INV_FC
Real number (ℝ)

High correlation  Skewed 

Distinct37378
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1028928.6
Minimum0
Maximum7.7989376 × 109
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2025-03-28T11:00:23.610958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q1122.05
median3346.56
Q323760
95-th percentile231000
Maximum7.7989376 × 109
Range7.7989376 × 109
Interquartile range (IQR)23637.95

Descriptive statistics

Standard deviation48083693
Coefficient of variation (CV)46.731807
Kurtosis16604.368
Mean1028928.6
Median Absolute Deviation (MAD)3345.56
Skewness119.34539
Sum9.9864725 × 1010
Variance2.3120415 × 1015
MonotonicityNot monotonic
2025-03-28T11:00:23.762957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0001 1431
 
1.5%
0.001 1351
 
1.4%
1 × 10-6983
 
1.0%
1 × 10-5940
 
1.0%
2 475
 
0.5%
1 397
 
0.4%
0.01 360
 
0.4%
10 352
 
0.4%
5 316
 
0.3%
3000 267
 
0.3%
Other values (37368) 90185
92.9%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 × 10-8180
0.2%
5 × 10-83
 
< 0.1%
9 × 10-81
 
< 0.1%
1 × 10-7256
0.3%
2 × 10-71
 
< 0.1%
3 × 10-71
 
< 0.1%
5 × 10-718
 
< 0.1%
6 × 10-71
 
< 0.1%
9 × 10-71
 
< 0.1%
ValueCountFrequency (%)
7798937590 1
< 0.1%
7749174697 1
< 0.1%
4258940906 2
< 0.1%
4044600000 1
< 0.1%
3394899200 2
< 0.1%
3007872900 1
< 0.1%
2374233260 1
< 0.1%
1636760000 1
< 0.1%
1186876250 1
< 0.1%
688517560 1
< 0.1%

FOB INR
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct63342
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1329140.6
Minimum-2348.4
Maximum6.4153726 × 108
Zeros9837
Zeros (%)10.1%
Negative6
Negative (%)< 0.1%
Memory size758.4 KiB
2025-03-28T11:00:23.903460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-2348.4
5-th percentile0
Q13596.4
median109557.11
Q3904453.6
95-th percentile5490232.3
Maximum6.4153726 × 108
Range6.4153961 × 108
Interquartile range (IQR)900857.2

Descriptive statistics

Standard deviation6353195.3
Coefficient of variation (CV)4.7799273
Kurtosis1930.8777
Mean1329140.6
Median Absolute Deviation (MAD)109557.11
Skewness32.405705
Sum1.290024 × 1011
Variance4.036309 × 1013
MonotonicityNot monotonic
2025-03-28T11:00:24.041459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9837
 
10.1%
1000 985
 
1.0%
2000 599
 
0.6%
3000 375
 
0.4%
4000 266
 
0.3%
132.7 221
 
0.2%
5000 210
 
0.2%
6000 189
 
0.2%
66.35 188
 
0.2%
66.6 164
 
0.2%
Other values (63332) 84023
86.6%
ValueCountFrequency (%)
-2348.4 1
 
< 0.1%
-778.18 1
 
< 0.1%
-389.09 1
 
< 0.1%
-194.54 3
 
< 0.1%
0 9837
10.1%
0.01 19
 
< 0.1%
0.02 13
 
< 0.1%
0.03 3
 
< 0.1%
0.04 5
 
< 0.1%
0.05 2
 
< 0.1%
ValueCountFrequency (%)
641537264 1
< 0.1%
446293100 1
< 0.1%
339372288 1
< 0.1%
318622539 1
< 0.1%
317609752 1
< 0.1%
284692804 1
< 0.1%
277308334 1
< 0.1%
253406250 1
< 0.1%
248863150 1
< 0.1%
235527973 1
< 0.1%
Distinct1165
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
2025-03-28T11:00:24.439458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length34
Median length20
Mean length8.4687555
Min length3

Characters and Unicode

Total characters821952
Distinct characters62
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique186 ?
Unique (%)0.2%

Sample

1st rowChittagong
2nd rowColombo
3rd rowRiyadh
4th rowVeracruz
5th rowJeddah
ValueCountFrequency (%)
singapore 3705
 
2.9%
london 3689
 
2.9%
birgunj 3080
 
2.4%
new 2859
 
2.3%
york 2843
 
2.3%
2780
 
2.2%
colombo 2438
 
1.9%
kathmandu 2088
 
1.7%
heathrow 2026
 
1.6%
island 1742
 
1.4%
Other values (1056) 98934
78.4%
2025-03-28T11:00:24.971957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 93034
 
11.3%
o 53113
 
6.5%
n 48985
 
6.0%
e 41139
 
5.0%
r 40544
 
4.9%
i 38236
 
4.7%
29642
 
3.6%
t 27404
 
3.3%
u 27012
 
3.3%
l 26482
 
3.2%
Other values (52) 396361
48.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 556395
67.7%
Uppercase Letter 228909
27.8%
Space Separator 29642
 
3.6%
Dash Punctuation 2728
 
0.3%
Other Punctuation 1901
 
0.2%
Open Punctuation 1324
 
0.2%
Close Punctuation 972
 
0.1%
Math Symbol 81
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 93034
16.7%
o 53113
 
9.5%
n 48985
 
8.8%
e 41139
 
7.4%
r 40544
 
7.3%
i 38236
 
6.9%
t 27404
 
4.9%
u 27012
 
4.9%
l 26482
 
4.8%
g 21228
 
3.8%
Other values (16) 139218
25.0%
Uppercase Letter
ValueCountFrequency (%)
O 17953
 
7.8%
A 17735
 
7.7%
N 17451
 
7.6%
M 16249
 
7.1%
S 15509
 
6.8%
L 13342
 
5.8%
H 12086
 
5.3%
C 11487
 
5.0%
E 11486
 
5.0%
K 11237
 
4.9%
Other values (16) 84374
36.9%
Other Punctuation
ValueCountFrequency (%)
/ 747
39.3%
, 524
27.6%
' 372
19.6%
. 130
 
6.8%
? 128
 
6.7%
Space Separator
ValueCountFrequency (%)
29642
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2728
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1324
100.0%
Close Punctuation
ValueCountFrequency (%)
) 972
100.0%
Math Symbol
ValueCountFrequency (%)
= 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 785304
95.5%
Common 36648
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 93034
 
11.8%
o 53113
 
6.8%
n 48985
 
6.2%
e 41139
 
5.2%
r 40544
 
5.2%
i 38236
 
4.9%
t 27404
 
3.5%
u 27012
 
3.4%
l 26482
 
3.4%
g 21228
 
2.7%
Other values (42) 368127
46.9%
Common
ValueCountFrequency (%)
29642
80.9%
- 2728
 
7.4%
( 1324
 
3.6%
) 972
 
2.7%
/ 747
 
2.0%
, 524
 
1.4%
' 372
 
1.0%
. 130
 
0.4%
? 128
 
0.3%
= 81
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 821952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 93034
 
11.3%
o 53113
 
6.5%
n 48985
 
6.0%
e 41139
 
5.0%
r 40544
 
4.9%
i 38236
 
4.7%
29642
 
3.6%
t 27404
 
3.3%
u 27012
 
3.3%
l 26482
 
3.2%
Other values (52) 396361
48.2%
Distinct212
Distinct (%)0.2%
Missing230
Missing (%)0.2%
Memory size5.4 MiB
2025-03-28T11:00:25.324458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length41
Median length27
Mean length9.2091875
Min length4

Characters and Unicode

Total characters891698
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowBANGLADESH
2nd rowSRI LANKA
3rd rowSAUDI ARABIA
4th rowMEXICO
5th rowSAUDI ARABIA
ValueCountFrequency (%)
united 16199
 
12.4%
states 9852
 
7.5%
nepal 5203
 
4.0%
kingdom 4693
 
3.6%
singapore 3706
 
2.8%
germany 2615
 
2.0%
sri 2438
 
1.9%
lanka 2438
 
1.9%
kenya 2275
 
1.7%
of 2273
 
1.7%
Other values (224) 79228
60.5%
2025-03-28T11:00:25.828459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 123203
13.8%
E 84398
 
9.5%
N 83433
 
9.4%
I 78816
 
8.8%
T 61000
 
6.8%
S 55251
 
6.2%
R 42997
 
4.8%
D 38542
 
4.3%
U 35217
 
3.9%
L 35080
 
3.9%
Other values (40) 253761
28.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 841990
94.4%
Space Separator 34093
 
3.8%
Lowercase Letter 13365
 
1.5%
Other Punctuation 2180
 
0.2%
Dash Punctuation 70
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 123203
14.6%
E 84398
10.0%
N 83433
9.9%
I 78816
 
9.4%
T 61000
 
7.2%
S 55251
 
6.6%
R 42997
 
5.1%
D 38542
 
4.6%
U 35217
 
4.2%
L 35080
 
4.2%
Other values (16) 204053
24.2%
Lowercase Letter
ValueCountFrequency (%)
o 1908
14.3%
c 1908
14.3%
i 1361
10.2%
e 1272
9.5%
a 813
 
6.1%
t 730
 
5.5%
n 724
 
5.4%
p 642
 
4.8%
g 642
 
4.8%
r 637
 
4.8%
Other values (8) 2728
20.4%
Other Punctuation
ValueCountFrequency (%)
. 1122
51.5%
, 1004
46.1%
' 51
 
2.3%
& 3
 
0.1%
Space Separator
ValueCountFrequency (%)
34093
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 855355
95.9%
Common 36343
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 123203
14.4%
E 84398
 
9.9%
N 83433
 
9.8%
I 78816
 
9.2%
T 61000
 
7.1%
S 55251
 
6.5%
R 42997
 
5.0%
D 38542
 
4.5%
U 35217
 
4.1%
L 35080
 
4.1%
Other values (34) 217418
25.4%
Common
ValueCountFrequency (%)
34093
93.8%
. 1122
 
3.1%
, 1004
 
2.8%
- 70
 
0.2%
' 51
 
0.1%
& 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 123203
13.8%
E 84398
 
9.5%
N 83433
 
9.4%
I 78816
 
8.8%
T 61000
 
6.8%
S 55251
 
6.2%
R 42997
 
4.8%
D 38542
 
4.3%
U 35217
 
3.9%
L 35080
 
3.9%
Other values (40) 253761
28.5%
Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
2025-03-28T11:00:26.235457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length48
Median length29
Mean length15.10229
Min length6

Characters and Unicode

Total characters1465783
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowNhava Sheva Sea
2nd rowNhava Sheva Sea
3rd rowHyderabad Air Cargo
4th rowNhava Sheva Sea
5th rowHazira
ValueCountFrequency (%)
air 52963
19.9%
cargo 52963
19.9%
sea 31590
11.9%
bombay 27436
10.3%
nhava 25697
9.6%
sheva 25697
9.6%
hyderabad 8205
 
3.1%
delhi 8104
 
3.0%
chennai 6417
 
2.4%
raxaul 4712
 
1.8%
Other values (69) 22692
8.5%
2025-03-28T11:00:26.619957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 246623
16.8%
171132
11.7%
r 128075
 
8.7%
o 89762
 
6.1%
e 88882
 
6.1%
i 74400
 
5.1%
h 71353
 
4.9%
C 63882
 
4.4%
S 58836
 
4.0%
A 58263
 
4.0%
Other values (42) 414575
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1005749
68.6%
Uppercase Letter 286248
 
19.5%
Space Separator 171132
 
11.7%
Other Punctuation 1086
 
0.1%
Dash Punctuation 906
 
0.1%
Open Punctuation 331
 
< 0.1%
Close Punctuation 331
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 246623
24.5%
r 128075
12.7%
o 89762
 
8.9%
e 88882
 
8.8%
i 74400
 
7.4%
h 71353
 
7.1%
g 58116
 
5.8%
v 51632
 
5.1%
b 40779
 
4.1%
y 36100
 
3.6%
Other values (14) 120027
11.9%
Uppercase Letter
ValueCountFrequency (%)
C 63882
22.3%
S 58836
20.6%
A 58263
20.4%
B 32130
11.2%
N 26766
9.4%
D 11685
 
4.1%
H 9550
 
3.3%
R 6004
 
2.1%
I 4514
 
1.6%
T 2905
 
1.0%
Other values (12) 11713
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 630
58.0%
/ 456
42.0%
Space Separator
ValueCountFrequency (%)
171132
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 906
100.0%
Open Punctuation
ValueCountFrequency (%)
( 331
100.0%
Close Punctuation
ValueCountFrequency (%)
) 331
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1291997
88.1%
Common 173786
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 246623
19.1%
r 128075
 
9.9%
o 89762
 
6.9%
e 88882
 
6.9%
i 74400
 
5.8%
h 71353
 
5.5%
C 63882
 
4.9%
S 58836
 
4.6%
A 58263
 
4.5%
g 58116
 
4.5%
Other values (36) 353805
27.4%
Common
ValueCountFrequency (%)
171132
98.5%
- 906
 
0.5%
, 630
 
0.4%
/ 456
 
0.3%
( 331
 
0.2%
) 331
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1465783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 246623
16.8%
171132
11.7%
r 128075
 
8.7%
o 89762
 
6.1%
e 88882
 
6.1%
i 74400
 
5.1%
h 71353
 
4.9%
C 63882
 
4.4%
S 58836
 
4.0%
A 58263
 
4.0%
Other values (42) 414575
28.3%

IEC
Real number (ℝ)

Distinct3129
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8689572 × 108
Minimum1.0000001 × 108
Maximum6.111002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2025-03-28T11:00:26.743958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1.0000001 × 108
5-th percentile3.0402392 × 108
Q13.8806326 × 108
median3.9903396 × 108
Q38.9600434 × 108
95-th percentile3.4080061 × 109
Maximum6.111002 × 109
Range6.011002 × 109
Interquartile range (IQR)5.0794108 × 108

Descriptive statistics

Standard deviation9.8226923 × 108
Coefficient of variation (CV)1.1075363
Kurtosis4.6320903
Mean8.8689572 × 108
Median Absolute Deviation (MAD)95013121
Skewness2.3165339
Sum8.6079438 × 1013
Variance9.6485284 × 1017
MonotonicityNot monotonic
2025-03-28T11:00:26.876957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388003791 2271
 
2.3%
509001220 1985
 
2.0%
388014482 1872
 
1.9%
2588000011 1592
 
1.6%
896004341 1465
 
1.5%
388026448 1428
 
1.5%
988002833 1283
 
1.3%
392072823 1242
 
1.3%
788012363 997
 
1.0%
895001721 973
 
1.0%
Other values (3119) 81949
84.4%
ValueCountFrequency (%)
100000011 141
0.1%
100000053 31
 
< 0.1%
100000061 9
 
< 0.1%
200000829 2
 
< 0.1%
200016601 1
 
< 0.1%
201004151 1
 
< 0.1%
201009404 3
 
< 0.1%
201011972 14
 
< 0.1%
202002080 3
 
< 0.1%
202018962 3
 
< 0.1%
ValueCountFrequency (%)
6111002040 1
 
< 0.1%
6107001085 2
 
< 0.1%
6107000526 1
 
< 0.1%
5613004307 3
 
< 0.1%
5311000929 14
< 0.1%
5299004184 9
< 0.1%
5297004136 4
 
< 0.1%
5297000033 14
< 0.1%
5296000404 2
 
< 0.1%
5295000036 7
< 0.1%
Distinct3170
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
2025-03-28T11:00:27.318958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length50
Median length41
Mean length24.746984
Min length3

Characters and Unicode

Total characters2401868
Distinct characters46
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique600 ?
Unique (%)0.6%

Sample

1st rowGALAXY SURFACTANTS LTD.,
2nd rowDADIA CHEMICAL INDUSTRIES
3rd rowRA CHEM PHARMA LIMITED
4th rowCHEMSPEC CHEMICALS PVT LTD
5th rowPAYAL PETROCHEM PRIVATE LIMITED
ValueCountFrequency (%)
ltd 37924
 
11.6%
limited 29003
 
8.9%
pvt 15808
 
4.9%
pharmaceuticals 12147
 
3.7%
laboratories 10062
 
3.1%
pharma 9321
 
2.9%
private 8746
 
2.7%
india 6289
 
1.9%
5807
 
1.8%
pvt.ltd 5456
 
1.7%
Other values (3434) 185000
56.8%
2025-03-28T11:00:27.907458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
238038
 
9.9%
A 232021
 
9.7%
I 207915
 
8.7%
T 192539
 
8.0%
E 191237
 
8.0%
L 164988
 
6.9%
R 142151
 
5.9%
D 118266
 
4.9%
S 115015
 
4.8%
M 100862
 
4.2%
Other values (36) 698836
29.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2076775
86.5%
Space Separator 238038
 
9.9%
Other Punctuation 78719
 
3.3%
Open Punctuation 3149
 
0.1%
Close Punctuation 3083
 
0.1%
Dash Punctuation 1804
 
0.1%
Decimal Number 234
 
< 0.1%
Modifier Symbol 66
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 232021
11.2%
I 207915
10.0%
T 192539
 
9.3%
E 191237
 
9.2%
L 164988
 
7.9%
R 142151
 
6.8%
D 118266
 
5.7%
S 115015
 
5.5%
M 100862
 
4.9%
C 96731
 
4.7%
Other values (16) 515050
24.8%
Other Punctuation
ValueCountFrequency (%)
. 56843
72.2%
, 16350
 
20.8%
& 5411
 
6.9%
% 44
 
0.1%
/ 35
 
< 0.1%
; 31
 
< 0.1%
: 5
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 92
39.3%
1 66
28.2%
3 49
20.9%
2 25
 
10.7%
9 1
 
0.4%
4 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 3143
99.8%
[ 6
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 3077
99.8%
] 6
 
0.2%
Space Separator
ValueCountFrequency (%)
238038
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1804
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2076775
86.5%
Common 325093
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 232021
11.2%
I 207915
10.0%
T 192539
 
9.3%
E 191237
 
9.2%
L 164988
 
7.9%
R 142151
 
6.8%
D 118266
 
5.7%
S 115015
 
5.5%
M 100862
 
4.9%
C 96731
 
4.7%
Other values (16) 515050
24.8%
Common
ValueCountFrequency (%)
238038
73.2%
. 56843
 
17.5%
, 16350
 
5.0%
& 5411
 
1.7%
( 3143
 
1.0%
) 3077
 
0.9%
- 1804
 
0.6%
0 92
 
< 0.1%
` 66
 
< 0.1%
1 66
 
< 0.1%
Other values (10) 203
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2401868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
238038
 
9.9%
A 232021
 
9.7%
I 207915
 
8.7%
T 192539
 
8.0%
E 191237
 
8.0%
L 164988
 
6.9%
R 142151
 
5.9%
D 118266
 
4.9%
S 115015
 
4.8%
M 100862
 
4.2%
Other values (36) 698836
29.1%
Distinct3121
Distinct (%)3.2%
Missing9
Missing (%)< 0.1%
Memory size8.9 MiB
2025-03-28T11:00:28.157959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length102
Median length79
Mean length47.368014
Min length7

Characters and Unicode

Total characters4596971
Distinct characters74
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique587 ?
Unique (%)0.6%

Sample

1st rowC-49/2,TTC INDUSTRIAL AREAPAWNE, NAVI MUMBAI.
2nd row201, SHIVAM , 3RD FLOOR, SATYAM SHOPPING CENTRE,M.G. ROAD
3rd row#.6-3-1239/2, AMAR HOUSE, 4TH FLOORRAJBHAWAN ROAD, SOMAJIGUDA,
4th row9 WALLACE ST FORT
5th rowE-24, NETAJI SUBHASH MARGDARYAGANJ
ValueCountFrequency (%)
road 22440
 
3.9%
floor 10465
 
1.8%
8869
 
1.5%
plot 8716
 
1.5%
no 6481
 
1.1%
marg 5305
 
0.9%
industrial 4948
 
0.9%
house 3972
 
0.7%
nagar 3778
 
0.7%
2nd 3674
 
0.6%
Other values (8556) 493846
86.3%
2025-03-28T11:00:28.548459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
503319
 
10.9%
A 489814
 
10.7%
R 292293
 
6.4%
, 267192
 
5.8%
O 242069
 
5.3%
E 237516
 
5.2%
N 221315
 
4.8%
I 214583
 
4.7%
T 182808
 
4.0%
L 170531
 
3.7%
Other values (64) 1775531
38.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3338670
72.6%
Space Separator 503319
 
10.9%
Other Punctuation 392263
 
8.5%
Decimal Number 285404
 
6.2%
Dash Punctuation 43158
 
0.9%
Open Punctuation 11984
 
0.3%
Close Punctuation 11581
 
0.3%
Lowercase Letter 10292
 
0.2%
Modifier Symbol 281
 
< 0.1%
Math Symbol 19
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 489814
14.7%
R 292293
 
8.8%
O 242069
 
7.3%
E 237516
 
7.1%
N 221315
 
6.6%
I 214583
 
6.4%
T 182808
 
5.5%
L 170531
 
5.1%
S 170365
 
5.1%
D 168891
 
5.1%
Other values (16) 948485
28.4%
Lowercase Letter
ValueCountFrequency (%)
ß 1370
13.3%
e 1200
11.7%
t 1090
10.6%
n 998
9.7%
r 958
9.3%
o 796
7.7%
s 729
7.1%
i 667
6.5%
l 592
 
5.8%
m 331
 
3.2%
Other values (9) 1561
15.2%
Other Punctuation
ValueCountFrequency (%)
, 267192
68.1%
. 96684
 
24.6%
/ 22641
 
5.8%
& 3783
 
1.0%
: 1026
 
0.3%
* 407
 
0.1%
# 318
 
0.1%
; 134
 
< 0.1%
? 64
 
< 0.1%
" 14
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 65850
23.1%
2 49039
17.2%
3 34030
11.9%
0 29864
10.5%
4 27906
9.8%
6 18374
 
6.4%
7 17392
 
6.1%
5 17154
 
6.0%
8 13636
 
4.8%
9 12159
 
4.3%
Open Punctuation
ValueCountFrequency (%)
( 11631
97.1%
[ 245
 
2.0%
{ 108
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 11228
97.0%
] 353
 
3.0%
Space Separator
ValueCountFrequency (%)
503319
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43158
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 281
100.0%
Math Symbol
ValueCountFrequency (%)
+ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3348962
72.9%
Common 1248009
 
27.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 489814
14.6%
R 292293
 
8.7%
O 242069
 
7.2%
E 237516
 
7.1%
N 221315
 
6.6%
I 214583
 
6.4%
T 182808
 
5.5%
L 170531
 
5.1%
S 170365
 
5.1%
D 168891
 
5.0%
Other values (35) 958777
28.6%
Common
ValueCountFrequency (%)
503319
40.3%
, 267192
21.4%
. 96684
 
7.7%
1 65850
 
5.3%
2 49039
 
3.9%
- 43158
 
3.5%
3 34030
 
2.7%
0 29864
 
2.4%
4 27906
 
2.2%
/ 22641
 
1.8%
Other values (19) 108326
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4595601
> 99.9%
None 1370
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
503319
 
11.0%
A 489814
 
10.7%
R 292293
 
6.4%
, 267192
 
5.8%
O 242069
 
5.3%
E 237516
 
5.2%
N 221315
 
4.8%
I 214583
 
4.7%
T 182808
 
4.0%
L 170531
 
3.7%
Other values (63) 1774161
38.6%
None
ValueCountFrequency (%)
ß 1370
100.0%

Address2
Unsupported

Missing  Rejected  Unsupported 

Missing97057
Missing (%)100.0%
Memory size758.4 KiB

City
Text

Distinct1289
Distinct (%)1.3%
Missing18
Missing (%)< 0.1%
Memory size6.2 MiB
2025-03-28T11:00:28.895458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length55
Median length33
Mean length17.389472
Min length3

Characters and Unicode

Total characters1687457
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique183 ?
Unique (%)0.2%

Sample

1st rowMAHARASHTRA
2nd rowGHATKOPAR (EAST), MUMBAI
3rd rowHYDERABAD, A.P.
4th rowBOMBAY ,MAHARASHTRA
5th rowNEW DELH I
ValueCountFrequency (%)
mumbai 33601
18.3%
maharashtra 30554
 
16.6%
delhi 6755
 
3.7%
ahmedabad 5286
 
2.9%
hyderabad 4655
 
2.5%
pradesh 4537
 
2.5%
gujarat 4526
 
2.5%
new 4033
 
2.2%
mumbai,maharashtra 3649
 
2.0%
bangalore 3384
 
1.8%
Other values (983) 82681
45.0%
2025-03-28T11:00:29.390458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 349473
20.7%
144468
 
8.6%
M 141871
 
8.4%
R 132605
 
7.9%
H 123512
 
7.3%
I 78322
 
4.6%
D 70072
 
4.2%
B 68588
 
4.1%
T 67903
 
4.0%
U 67327
 
4.0%
Other values (59) 443316
26.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1422025
84.3%
Space Separator 144468
 
8.6%
Other Punctuation 100926
 
6.0%
Lowercase Letter 7158
 
0.4%
Close Punctuation 4510
 
0.3%
Open Punctuation 4510
 
0.3%
Dash Punctuation 2506
 
0.1%
Decimal Number 1354
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 349473
24.6%
M 141871
10.0%
R 132605
 
9.3%
H 123512
 
8.7%
I 78322
 
5.5%
D 70072
 
4.9%
B 68588
 
4.8%
T 67903
 
4.8%
U 67327
 
4.7%
E 61728
 
4.3%
Other values (16) 260624
18.3%
Lowercase Letter
ValueCountFrequency (%)
e 719
 
10.0%
a 688
 
9.6%
l 595
 
8.3%
o 547
 
7.6%
n 547
 
7.6%
r 468
 
6.5%
t 468
 
6.5%
y 423
 
5.9%
i 344
 
4.8%
d 344
 
4.8%
Other values (11) 2015
28.2%
Decimal Number
ValueCountFrequency (%)
1 560
41.4%
0 362
26.7%
8 130
 
9.6%
6 113
 
8.3%
5 99
 
7.3%
4 38
 
2.8%
2 27
 
2.0%
3 13
 
1.0%
7 6
 
0.4%
9 6
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 60213
59.7%
. 32560
32.3%
/ 7732
 
7.7%
& 209
 
0.2%
: 205
 
0.2%
? 7
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 3916
86.8%
] 594
 
13.2%
Open Punctuation
ValueCountFrequency (%)
( 3916
86.8%
[ 594
 
13.2%
Space Separator
ValueCountFrequency (%)
144468
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1429183
84.7%
Common 258274
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 349473
24.5%
M 141871
9.9%
R 132605
 
9.3%
H 123512
 
8.6%
I 78322
 
5.5%
D 70072
 
4.9%
B 68588
 
4.8%
T 67903
 
4.8%
U 67327
 
4.7%
E 61728
 
4.3%
Other values (37) 267782
18.7%
Common
ValueCountFrequency (%)
144468
55.9%
, 60213
23.3%
. 32560
 
12.6%
/ 7732
 
3.0%
) 3916
 
1.5%
( 3916
 
1.5%
- 2506
 
1.0%
[ 594
 
0.2%
] 594
 
0.2%
1 560
 
0.2%
Other values (12) 1215
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1687255
> 99.9%
None 202
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 349473
20.7%
144468
 
8.6%
M 141871
 
8.4%
R 132605
 
7.9%
H 123512
 
7.3%
I 78322
 
4.6%
D 70072
 
4.2%
B 68588
 
4.1%
T 67903
 
4.0%
U 67327
 
4.0%
Other values (58) 443114
26.3%
None
ValueCountFrequency (%)
ß 202
100.0%

ForeignCompany
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size6.7 MiB

Invoice_No
Unsupported

Rejected  Unsupported 

Missing2
Missing (%)< 0.1%
Memory size5.0 MiB

CUSH
Text

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
2025-03-28T11:00:29.618958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters582342
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowINNSA1
2nd rowINNSA1
3rd rowINHYD4
4th rowINNSA1
5th rowINHZA1
ValueCountFrequency (%)
inbom4 27435
28.3%
innsa1 25697
26.5%
indel4 8104
 
8.3%
inhyd4 6399
 
6.6%
inrxlb 4712
 
4.9%
inmaa4 4148
 
4.3%
inblr4 3261
 
3.4%
inamd4 2869
 
3.0%
inmaa1 2269
 
2.3%
insnf6 1806
 
1.9%
Other values (55) 10357
 
10.7%
2025-03-28T11:00:30.021959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 126119
21.7%
I 98786
17.0%
4 52963
9.1%
A 43218
 
7.4%
M 38243
 
6.6%
B 37595
 
6.5%
1 32474
 
5.6%
S 29369
 
5.0%
O 28519
 
4.9%
D 19000
 
3.3%
Other values (19) 76056
13.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 490220
84.2%
Decimal Number 92122
 
15.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 126119
25.7%
I 98786
20.2%
A 43218
 
8.8%
M 38243
 
7.8%
B 37595
 
7.7%
S 29369
 
6.0%
O 28519
 
5.8%
D 19000
 
3.9%
L 16551
 
3.4%
R 8705
 
1.8%
Other values (16) 44115
 
9.0%
Decimal Number
ValueCountFrequency (%)
4 52963
57.5%
1 32474
35.3%
6 6685
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 490220
84.2%
Common 92122
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 126119
25.7%
I 98786
20.2%
A 43218
 
8.8%
M 38243
 
7.8%
B 37595
 
7.7%
S 29369
 
6.0%
O 28519
 
5.8%
D 19000
 
3.9%
L 16551
 
3.4%
R 8705
 
1.8%
Other values (16) 44115
 
9.0%
Common
ValueCountFrequency (%)
4 52963
57.5%
1 32474
35.3%
6 6685
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 582342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 126119
21.7%
I 98786
17.0%
4 52963
9.1%
A 43218
 
7.4%
M 38243
 
6.6%
B 37595
 
6.5%
1 32474
 
5.6%
S 29369
 
5.0%
O 28519
 
4.9%
D 19000
 
3.3%
Other values (19) 76056
13.1%

IEC_PIN
Unsupported

Missing  Rejected  Unsupported 

Missing2341
Missing (%)2.4%
Memory size3.6 MiB

Item_No
Real number (ℝ)

High correlation 

Distinct266
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.443307
Minimum1
Maximum281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size758.4 KiB
2025-03-28T11:00:30.159959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q37
95-th percentile54
Maximum281
Range280
Interquartile range (IQR)6

Descriptive statistics

Standard deviation24.050746
Coefficient of variation (CV)2.3029819
Kurtosis30.915973
Mean10.443307
Median Absolute Deviation (MAD)1
Skewness4.8766405
Sum1013596
Variance578.43839
MonotonicityNot monotonic
2025-03-28T11:00:30.461958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 40011
41.2%
2 12517
 
12.9%
3 7226
 
7.4%
4 4984
 
5.1%
5 3599
 
3.7%
6 2780
 
2.9%
7 2173
 
2.2%
8 1796
 
1.9%
9 1520
 
1.6%
10 1302
 
1.3%
Other values (256) 19149
19.7%
ValueCountFrequency (%)
1 40011
41.2%
2 12517
 
12.9%
3 7226
 
7.4%
4 4984
 
5.1%
5 3599
 
3.7%
6 2780
 
2.9%
7 2173
 
2.2%
8 1796
 
1.9%
9 1520
 
1.6%
10 1302
 
1.3%
ValueCountFrequency (%)
281 2
< 0.1%
280 2
< 0.1%
279 2
< 0.1%
278 2
< 0.1%
277 2
< 0.1%
276 2
< 0.1%
272 3
< 0.1%
270 3
< 0.1%
266 3
< 0.1%
263 3
< 0.1%

Item_Rate_INR
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct64777
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38399.905
Minimum-3890.9
Maximum1.3233761 × 108
Zeros11676
Zeros (%)12.0%
Negative6
Negative (%)< 0.1%
Memory size758.4 KiB
2025-03-28T11:00:30.631459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-3890.9
5-th percentile0
Q124
median145.0955
Q31097.4125
95-th percentile55296.03
Maximum1.3233761 × 108
Range1.323415 × 108
Interquartile range (IQR)1073.4125

Descriptive statistics

Standard deviation798886.86
Coefficient of variation (CV)20.804397
Kurtosis13543.916
Mean38399.905
Median Absolute Deviation (MAD)145.0955
Skewness99.114969
Sum3.7269796 × 109
Variance6.3822022 × 1011
MonotonicityNot monotonic
2025-03-28T11:00:30.810959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11676
 
12.0%
1000 459
 
0.5%
333.3333333 215
 
0.2%
132.7 210
 
0.2%
500 207
 
0.2%
66.35 184
 
0.2%
66.6 162
 
0.2%
133.2 159
 
0.2%
100 138
 
0.1%
250 115
 
0.1%
Other values (64767) 83532
86.1%
ValueCountFrequency (%)
-3890.9 1
 
< 0.1%
-3890.8 3
 
< 0.1%
-2348.4 1
 
< 0.1%
-778.18 1
 
< 0.1%
0 11676
12.0%
7 × 10-61
 
< 0.1%
8 × 10-61
 
< 0.1%
8.82 × 10-61
 
< 0.1%
9.62 × 10-62
 
< 0.1%
9.8 × 10-61
 
< 0.1%
ValueCountFrequency (%)
132337611.1 1
< 0.1%
112631170 1
< 0.1%
83127705 1
< 0.1%
56307570 1
< 0.1%
37538380 1
< 0.1%
33251080 1
< 0.1%
33004333.33 2
< 0.1%
32977521 1
< 0.1%
31842500 1
< 0.1%
24841250 1
< 0.1%

Interactions

2025-03-28T11:00:18.279958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:08.976457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:09.981958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:11.027457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:12.069457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:13.168958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:14.183958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:15.248958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:16.251959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:17.287958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:18.381460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:09.080458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:10.086958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:11.126957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:12.167459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:13.268959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:14.285959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:15.344958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:16.351957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:17.386959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:18.482957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:09.181458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:10.190958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:11.227957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:12.275459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:13.377958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:14.391457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:15.446958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:16.464459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:17.484458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:18.593959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:09.279457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:10.295958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:11.324960image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:12.482459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:13.478460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:14.496458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:15.545459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:16.566457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:17.589460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:18.692957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:09.375457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:10.395960image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:11.426958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:12.574958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:13.577958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:14.604959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:15.641457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:16.667459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:17.683958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:18.927459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:09.474459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:10.494959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:11.524959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:12.671958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:13.674458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:14.713458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:15.745458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:16.768458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:17.780957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:19.038459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:09.584958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:10.616459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:11.638957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:12.778459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:13.783458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:14.827458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:15.851456image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:16.874958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:17.888460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:19.138960image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:09.682957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:10.726457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:11.752458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:12.876459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:13.883958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:14.930457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:15.949959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:16.984958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:17.987958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:19.242459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:09.782461image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:10.825459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:11.853958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:12.973459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:13.983957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:15.036458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:16.049957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:17.080957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:18.085958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:19.351460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:09.877460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:10.924958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:11.960457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:13.067459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:14.080957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:15.136458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:16.145458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:17.182958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T11:00:18.177458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-03-28T11:00:30.939460image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
4DigitBillNOCurrencyFOB INRHSCodeIECItem_NoItem_Rate_INRItem_Rate_INVQuantityTotal_Amount_INV_FCUnit
4Digit1.0000.0180.085-0.1220.8990.0290.385-0.340-0.3780.222-0.1280.448
BillNO0.0181.0000.0320.0910.011-0.011-0.0200.0730.0050.0140.0250.131
Currency0.0850.0321.0000.0000.0850.0600.0330.0000.0760.0000.0340.095
FOB INR-0.1220.0910.0001.000-0.1210.060-0.5290.4350.3250.6520.8550.034
HSCode0.8990.0110.085-0.1211.0000.0450.367-0.311-0.3470.195-0.1280.448
IEC0.029-0.0110.0600.0600.0451.000-0.0310.1080.091-0.0040.0350.139
Item_No0.385-0.0200.033-0.5290.367-0.0311.000-0.237-0.240-0.295-0.5080.086
Item_Rate_INR-0.3400.0730.0000.435-0.3110.108-0.2371.0000.795-0.2550.2720.023
Item_Rate_INV-0.3780.0050.0760.325-0.3470.091-0.2400.7951.000-0.2490.4460.027
Quantity0.2220.0140.0000.6520.195-0.004-0.295-0.255-0.2491.0000.6820.019
Total_Amount_INV_FC-0.1280.0250.0340.855-0.1280.035-0.5080.2720.4460.6821.0000.000
Unit0.4480.1310.0950.0340.4480.1390.0860.0230.0270.0190.0001.000

Missing values

2025-03-28T11:00:19.540957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-28T11:00:19.978458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-28T11:00:20.381961image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BillNO4DigitDateHSCodeProductQuantityUnitItem_Rate_INVCurrencyTotal_Amount_INV_FCFOB INRForeignPortForeignCountryIndianPortIECIndianCompanyAddress1Address2CityForeignCompanyInvoice_NoCUSHIEC_PINItem_NoItem_Rate_INR
0800143929152016-06-0129157090ETHYLENE GLYCOL DISTERATE (Assay 98% minimum) (EGDS)2400.000KGS2.855USD6852.000000435999.12ChittagongBANGLADESHNhava Sheva Sea388162040GALAXY SURFACTANTS LTD.,C-49/2,TTC INDUSTRIAL AREAPAWNE, NAVI MUMBAI.NaNMAHARASHTRAUNILEVER BANGLADESH LTD.80206964INNSA14007031181.666300
1802199729152016-06-0129159090COSMOSIL IM2100.000KGS5.750USD12075.000000814446.25ColomboSRI LANKANhava Sheva Sea396073964DADIA CHEMICAL INDUSTRIES201, SHIVAM , 3RD FLOOR, SATYAM SHOPPING CENTRE,M.G. ROADNaNGHATKOPAR (EAST), MUMBAIHARUMI HOLDINGS (PVT) LTD,DCI/EXP-03/16-17INNSA1PIN-4000771387.831548
2801978629162016-06-0129163190MEBEVERINE HCL BP750.000KGS85.000USD63750.0000004134001.50RiyadhSAUDI ARABIAHyderabad Air Cargo996000836RA CHEM PHARMA LIMITED#.6-3-1239/2, AMAR HOUSE, 4TH FLOORRAJBHAWAN ROAD, SOMAJIGUDA,NaNHYDERABAD, A.P.MEDICAL & COSMETIC PRODUCTS CO.LTDEXP/A106/16-17INHYD450008215512.002000
3800650729162016-06-0129163990WE INTEND TO CLAIM REWARDS UNDER MERCHANDISE EXPORTS FROM INDIA SCHEME (MEIS)0.001NOS0.001USD0.0000010.00VeracruzMEXICONhava Sheva Sea388170247CHEMSPEC CHEMICALS PVT LTD9 WALLACE ST FORTNaNBOMBAY ,MAHARASHTRACOSBEL S.A. DE C.V.CSEXP/2016-17/143INNSA140000120.000000
4801521029172016-06-0129171990"Payflex-M-80" Di Octyl Maleate Ester, C44000.000KGS1.180USD51920.00000034.09JeddahSAUDI ARABIAHazira511004346PAYAL PETROCHEM PRIVATE LIMITEDE-24, NETAJI SUBHASH MARGDARYAGANJNaNNEW DELH IS. S. C. I.. C. L.PM/EXP/048INHZA1NaN10.000000
5801752729172016-06-0129171990LABORATORY CHEMICAL - SODIUM SUCCINATE5600.000KGS95.000USD532000.000000705964.00Port KelangMALAYSIANhava Sheva Sea388045485S. D. FINE CHEM LTD.315-317, T. V. INDUSTRIAL ESTATE,248, WORLI ROAD,NaNMUMBAIBIOCON SDN BHDCOR/9126INNSA14000251126.065000
6801583129172016-06-0129173910"Payflex-P-400" Di Butyl Phthalate (DBP)66000.000KGS0.990USD65340.00000042.20DammamSAUDI ARABIAHazira511004346PAYAL PETROCHEM PRIVATE LIMITEDE-24, NETAJI SUBHASH MARGDARYAGANJNaNNEW DELH II. D. C.PM/EXP/049INHZA1NaN10.000000
7800825929172016-06-0129173920DI OCTYL PHTHALATE (DOP) (AS PER INV.)20000.000KGS1.180USD23600.0000001432231.10HoustonUNITED STATESNhava Sheva Sea596059035KLJ PLASTICIZERS LIMITEDKLJ HOUSE 63 RAMA MARGNAJAFGARH ROADNaNNEW DELHII.C.I.KP/16-17/114INNSA1110015171.611555
8801285229152016-06-01291590902-ETHYL HEXANOYL CHLORIDE MIN.99.00% "(OTHER SATRTD ACYLC MNOCRBIXYLC ACDS ETCAND THR DRVTVS) WE INTEND TO CLAIM REWA15200.000KGS2.400USD36480.0000002403197.00RotterdamNETHERLANDSNhava Sheva Sea3401001213SHIVA PHARMACHEM LTD,8TH FLOOR, ABS TOWERS OLD PADRA RDNaNBARODASHIVA PHARMACHEM AG9160140036INNSA13900071158.105066
9800594129172016-06-0129173940CHEMICAL: DIMETHYL PHTHALATE (DMP) IN IS26000.000KGS1.220USD31720.0000001794000.00GothenburgUNITED STATESMundra Sea389029971AARTI INDUSTRIES LIMITEDUDYOG KSHETRA,2ND FLOOR,MULUNDGOREGAON LINK ROAD,MULUND (W),NaNMUMBAI / MAHARASHTRAUNITED INITIATORS ABEX/1049/16-17INMUN140008010.000000
BillNO4DigitDateHSCodeProductQuantityUnitItem_Rate_INVCurrencyTotal_Amount_INV_FCFOB INRForeignPortForeignCountryIndianPortIECIndianCompanyAddress1Address2CityForeignCompanyInvoice_NoCUSHIEC_PINItem_NoItem_Rate_INR
97047860326130042016-06-3030049039FEXOFENADINE HYDROCHLORIDE TABLETS 180MG(PHARMACIST FORMULA FEXOFENADINE 180MGTABLETS) 3X10S5124.0PAC2.78AUD14244.72694430.03SydneyAUSTRALIABangalore791000770MEDREICH LIMITEDMEDREICH HOUSE, NO12/8,SARASWATHI AMMAL STREET, MARUTHINaNSEVA NAGAR, BANGALORE, KARNATAKAM/S. MEDREICH AUSTRALIA8150015579INWFD65600334135.524986
97048860331930042016-06-3030045039IRON+FOLIC ACID+VITAMIN B12 SYRUP (BIOFERON LIQUID) 200ML8556.0PAC0.65USD5561.40368998.89Le HavreFRANCEBangalore791000770MEDREICH LIMITEDMEDREICH HOUSE, NO12/8,SARASWATHI AMMAL STREET, MARUTHINaNSEVA NAGAR, BANGALORE, KARNATAKAM/S. SANOFI AVENTIS8150015557INWFD6560033143.127500
97049860335130042016-06-3030049099TIMOLOL EYE DROPS BP 0.5% W/V (TIMOL 0.5%) 10ML8550.0PAC0.21USD1795.50119131.43Le HavreFRANCEBangalore791000770MEDREICH LIMITEDMEDREICH HOUSE, NO12/8,SARASWATHI AMMAL STREET, MARUTHINaNSEVA NAGAR, BANGALORE, KARNATAKAM/S. SANOFI AVENTIS8150015578INWFD6560033213.933501
97050860335730042016-06-3030041090MARINA-1000 - FISH LIPID OIL CAPSULES 1000MG (X2X15'S) (AS PER INVOICE) MFD BY STRIDES SHASUN LTD.ORAL DOSAGE FORMS51665.0NOS0.70USD36165.502321287.93ApapaNIGERIABangalore390012441STRIDES SHASUN LIMITED206, DEVAVRATA,SECTOR 17, VASHI,NaNNAVI MUMBAI, MAHARASHTRA.M/S.WORLD WIDE COMMERCIAL VENTURES4139007618INWFD6400705144.929603
97051860336130042016-06-3030041090ERYTHROMYCIN TABLETS BP 250MG (X2X14'S)(AS PER INVOICE) MFD BY STRIDES SHASUN LTD. ORAL DOSAGE FORMS102932.0NOS0.55GBP56612.604663798.94FelixstoweUNITED KINGDOMBangalore390012441STRIDES SHASUN LIMITED206, DEVAVRATA,SECTOR 17, VASHI,NaNNAVI MUMBAI, MAHARASHTRA.M/S.CO-PHARMA LIMITED,4139007622INWFD6400705145.309514
97052860336130042016-06-3030041090INDAPAMIDE TABLETS 2.5MG (X4X7'S) (AS PER INVOICE) MFD BY STRIDES SHASUN LTD. ORAL DOSAGE FORMS126794.0NOS0.38GBP48181.723969255.86FelixstoweUNITED KINGDOMBangalore390012441STRIDES SHASUN LIMITED206, DEVAVRATA,SECTOR 17, VASHI,NaNNAVI MUMBAI, MAHARASHTRA.M/S.CO-PHARMA LIMITED,4139007622INWFD6400705231.304761
97053860336330042016-06-3030041090BUSPIRONE HYDROCHLORIDE TABLETS 5MG (3X10'S) (AS PER INVOICE) MFD BY STRIDES SHASUN LTD. ORAL DOSAGE FORMS10664.0NOS1.50GBP15996.001363018.90FelixstoweUNITED KINGDOMBangalore390012441STRIDES SHASUN LIMITED206, DEVAVRATA,SECTOR 17, VASHI,NaNNAVI MUMBAI, MAHARASHTRA.M/S.CO-PHARMA LIMITED,KRSG-301INWFD64007053127.814976
97054860344730042016-06-3030042019CEFOTAXIME SODIUM FOR INJECTION 1G (ZOTAX) 1+1S21600.0PAC0.56USD12096.00802569.60AdenYEMEN DEMOCRATICBangalore791000770MEDREICH LIMITEDMEDREICH HOUSE, NO12/8,SARASWATHI AMMAL STREET, MARUTHINaNSEVA NAGAR, BANGALORE, KARNATAKAM/S. AL JABAR PHARMA FOR DRUGS & ME8150015502INWFD6560033137.156000
97055860345230042016-06-3030041090PREDSONE - PREDNISONE TABLETS 1MG (X100TABS) (AS PER INVOICE) MFD BY STRIDES SHASUN LTD.ORAL DOSAGE FORMS19420.0NOS0.93AUD18060.60869483.90SydneyAUSTRALIABangalore390012441STRIDES SHASUN LIMITED206, DEVAVRATA,SECTOR 17, VASHI,NaNNAVI MUMBAI, MAHARASHTRA.M/S.ASPEN PHARMACARE PTY LTD,4139007609INWFD6400705244.772600
97056860345230042016-06-3030041090PERIACTIN - CYPROHEPTADINE TABLETS 4MG (X10X10'S) (AS PER INVOICE) MFD BY STRIDES SHASUN LTD. ORAL DOSAGE FORMS11587.0NOS2.73AUD31632.511522870.39SydneyAUSTRALIABangalore390012441STRIDES SHASUN LIMITED206, DEVAVRATA,SECTOR 17, VASHI,NaNNAVI MUMBAI, MAHARASHTRA.M/S.ASPEN PHARMACARE PTY LTD,4139007609INWFD64007055131.429222

Duplicate rows

Most frequently occurring

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1803710329292016-06-0229291020TOLUENE DI ISOCYANATE 80/2020.0MTS1953.000USD39060.025.48MombasaKENYAHazira888000685GUJARAT NARMADA VALLEY FERTILIZERS & CHEMICALS LTDP O NARMADANAGARDIST BHARUCH ,GUJRATINHZA110.000005
25838024729292016-06-2029291020TOLUENE DIISOCYANATE 80:2020.0MTS2260.000USD45200.029.81Alexandra(Egypt)EGYPTHazira3406000321PALVI POWER TECH SALES P LTD228 ARPAN COMPLEX NR.HANUMAN TEMPLENIZAMPURABARODA (GJ)INHZA110.000005
29841259929292016-06-2229291020TOLUENE DIISOCYANATE 80:2020.0MTS2240.000USD44800.029.62Jebel AliUNITED ARAB EMIRATESHazira3406000321PALVI POWER TECH SALES P LTD228 ARPAN COMPLEX NR.HANUMAN TEMPLENIZAMPURABARODA (GJ)INHZA110.000005
30842829329292016-06-2229291020TOLUENE DI ISOCYANATE (80/20)20.0MTS1660.000USD33200.021.64Jebel AliUNITED ARAB EMIRATESHazira804007705DASALDHAN CHEMICALS PVT. LIMITEDB/4, VIJAY TOWER, OPP: ABADDAIRY, KANKARIA,AHMEDABADINHZA110.000005
36859967929292016-06-3029291020TOLUENE DIISOCYANATE 80:2020.0MTS2260.000USD45200.029.83Alexandra(Egypt)EGYPTHazira3406000321PALVI POWER TECH SALES P LTD228 ARPAN COMPLEX NR.HANUMAN TEMPLENIZAMPURABARODA (GJ)INHZA110.000005
2803787029292016-06-0229291020TOLUENE DI-ISOCYNATE (TDI 80/20)20000.0KGS2.150USD43000.027.88ApapaNIGERIAHazira3402001152PRAKASH CHEMICALS INTERNATIONAL PVT. LIMITEDINDUCHACHA HOUSE OPP-CHHANI OCTROINAKABARODAINHZA110.000004
22833718829212016-06-1729212910HEXAMINE (UN-STABILIZED)18.0MTS1207.000USD21726.01281450.73HoustonUNITED STATESAnkleshwar288000994KANORIA CHEMICALS & INDUSTRIES LTDPARK PLAZA71 PARK STREETCALCUTTA, WEST BENGALINAKV6171191.707224
23833916929292016-06-1729291020TOLUENE DI-ISOCYNATE (TDI 80/20)20000.0KGS2.185USD43700.028.63MombasaKENYAHazira3402001152PRAKASH CHEMICALS INTERNATIONAL PVT. LIMITEDINDUCHACHA HOUSE OPP-CHHANI OCTROINAKABARODAINHZA110.000004